Abstract
This paper delves into the application of Stochastic Model Predictive Controls (SMPC) for power grids driven by inverter-interfaced generators, focusing on enhancing grid stability amidst decreasing inertia. By employing SMPC, uncertainties in energy systems are anticipated and plant-model mismatch is mitigated. Improvement in grid robustness concerning frequency limits is demonstrated via a Monte Carlo approach. The integration of data-driven model augmentation and stochastic constraint tightening significantly enhance the precision and robustness of frequency control. This study highlights the potential of SMPC in navigating uncertainties in energy systems and offering a robust framework for maintaining grid stability.
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